1. Field of the Invention
The invention relates to a method and apparatus for the implementation of a computerized scheme used in the quantitative analysis of interstitial lung diseases through the provision of a fully automated method and system where a large number of regions of interest (ROIs) covering large peripheral areas of the lungs are selected. A greater number of texture measures are taken in order to discriminate normal lungs from abnormal lungs having interstitial diseases.
2. Discussion of Background
Evaluation of interstitial disease from chest radiographs is one of the most difficult tasks for diagnostic radiologists. This difficulty is related to the numerous patterns and complex variations in the X-ray images, the lack of a firmly established correlation between radiologic and pathologic findings, and the subjective terms used in the description of various patterns. In recent years, digital chest radiography has been implemented in a computerized scheme which has been shown to be capable of detecting with accuracy interstitial diseases of the lungs. The computerized method can detect potentially abnormal lung texture patterns on the basis of quantitative measurements of the severity of abnormalities, and the subjectivity involved in the evaluation can be reduced as the accuracy of radiologic interpretation is increased.
In order to detect and characterize interstitial disease, there has recently been developed a computerized scheme, based on Fourier analysis techniques, for quantifying lung textures in digital chest radiographs. Such a method is disclosed in U.S. Pat. No. 4,839,807 to Doi et al, incorporated herein by reference. In this method, a conventional posterior-anterior (PA) chest radiograph is digitized with a Fuji drum scanner system employing a 0.1 mm pixel size and a 10-bit gray scale. Approximately 20 square regions of interest (ROIs) with a 64.times.64 matrix size are selected from the intercostal spaces. Manually interactive operations are needed in the ROI selection for the avoidance of ribs. A non-uniform background trend caused by the gross anatomy of the lung and chest wall is corrected by fitting a two-dimensional surface to the original image in an ROI and subtraction of the fitted surface from the original image. Such a surface-fitting technique facilitates the determination of fluctuating patterns of the underlying lung texture for subsequent analysis and processing by a computer.
The root mean square (RMS) variation, also referred to as R, and the first moment of the power spectrum, commonly referred to as M, are then determined, by use of the two-dimensional Fourier transform, as quantitative measures of the magnitude and coarseness (or fineness), respectively, of the lung texture. The two-dimensional Fourier transformed data are defined in terms of a function T(u,v) where u and v are spatial frequencies in a Cartesian coordinate system. The function T(u,v) is band-pass filtered by another function known in the art as the human visual response V(u,v) as a means of suppressing low frequency and high frequency components, in order to enhance differences between normal and abnormal lungs.
From the filtered data (T(u,v), V(u,v)) the two texture measures R and M are obtained for each ROI. The ROIs are then classified as normal or abnormal on the basis of a comparison of these texture measures and a database derived from clinical cases. The database is obtained by determining average R and M values from lungs which were predetermined to be normal or abnormal. The normal lungs on average showed R values which were lower than those for the abnormal lungs and M values which were higher. The results are displayed on a CRT monitor, providing a "second opinion" as an aid to radiologists in their interpretation.
On the monitor, symbols that indicate the severity and pattern type of interstitial diseases are superimposed on a digitized version of the original radiograph. If an analyzed lung is determined to be abnormal based on the texture levels of R and M being higher or lower than threshold levels, each individual ROI having such abnormal R, M values is indicated on the monitor screen with either a circle (representing a nodular pattern), a square (representing a reticular pattern), or a hexagon (representing honeycomb or reticulo-nodular patterns). The magnitude or severity of the abnormal ROI is proportional to the size of the pattern on the screen. The estimated probability of normal (or abnormal) lungs for a given chest image is also provided based on the classification results of these ROIs and on their geometric locations in the lung. Such probability estimations are provided by receiver operating characteristic (ROC) curves which are curves representative of the relationship between the fraction of true-positive determinations of abnormal lungs, i.e., an abnormal diagnosis for an abnormal lung, and the fraction of false-positive determinations, i.e., an abnormal diagnosis for a normal lung. A comparison of ROC curves obtained by radiologists and by this computerized scheme suggests that the computerized approach can provide performance similar to that of human observers in distinguishing lungs with mild interstitial diseases from normal lungs. Thus, the computerized scheme can be used by radiologists as a means of checking their initial diagnoses. In this manner, false negatives may be reduced and the diagnostic accuracy improved by the use of this computer-aided scheme.
In the method discussed above, however, it is necessary to be able to select a large number of ROIs covering major peripheral portions of the lungs in order to provide a greater likelihood of detecting abnormal ROIs which may exist only in small, isolated regions of the lung. Thus, for implementation of the computerized scheme in practical clinical situations, it is required to select numerous adjacent ROIs of a digitized chest image and also to automate the selection process.